Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is used). This paper focuses on a novel problem within this field, namely Model Provenance (MP), which concerns the relationship between a target model and its pre-training model and aims to determine whether a source model serves as the provenance for a target model. This is an important problem that has significant implications for ensuring the security and intellectual property of machine learning models but has not received much attention in the literature. To fill in this gap, we introduce a novel concept of Model DNA which represents the unique characteristics of a machine learning model. We utilize a data-driven and model-driven representation learning method to encode the model's training data and input-output information as a compact and comprehensive representation (i.e., DNA) of the model. Using this model DNA, we develop an efficient framework for model provenance identification, which enables us to identify whether a source model is a pre-training model of a target model. We conduct evaluations on both computer vision and natural language processing tasks using various models, datasets, and scenarios to demonstrate the effectiveness of our approach in accurately identifying model provenance.

翻译：理解机器学习模型的生命周期是一个引人入胜的研究领域（例如，理解模型从何而来、如何训练以及如何使用）。本文聚焦于该领域中的一个新问题，即模型溯源，该问题关注目标模型与其预训练模型之间的关系，旨在判断源模型是否为目标模型的来源。这是一个重要问题，对于确保机器学习模型的安全性和知识产权具有重要意义，但目前在文献中尚未得到充分关注。为填补这一空白，我们引入了模型DNA这一新概念，它代表了机器学习模型的独特特征。我们利用数据驱动和模型驱动的表征学习方法，将模型的训练数据与输入输出信息编码为模型的紧凑且全面的表征（即DNA）。借助模型DNA，我们开发了一个高效的模型溯源识别框架，能够判断源模型是否为目标模型的预训练模型。我们在计算机视觉和自然语言处理任务中，使用多种模型、数据集和场景进行了评估，证明了我们的方法在准确识别模型溯源方面的有效性。